A Compact V-band power detector

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A compact V-band active power detector using Infineon 0.35 µm SiGe HBT process (fT/fmax =170/250 GHz) is described. The total chip area is only 0.35×0.8 mm2 including all pads. This design exhibits a dynamic range larger than 20 dB over the frequency range from 55 GHz to 67 GHz. It also offers a simple and low-power application potential as an envelop detector in multi-Gbps high data rate demodulators for OOK/ASK etc.

A latent feature analysis of the neural representation of conceptual knowledge

Bayesian probabilistic analysis offers a new approach to characterize semantic representations by inferring the most likely feature structure directly from the patterns of brain activity. In this study, infinite latent feature models [1] are used to recover the semantic features that give rise to the brain activation vectors when people think about properties associated with 60 concrete concepts. The semantic features recovered by ILFM are consistent with the human ratings of the shelter, manipulation, and eating factors that were recovered by a previous factor analysis. Furthermore, different areas of the brain encode different perceptual and conceptual features. This neurally-inspired semantic representation is consistent with some existing conjectures regarding the role of different brain areas in processing different semantic and perceptual properties. © 2012 Springer-Verlag.

Real-Time Face Recognition from Surveillance Video

This chapter describes an experimental system for the recognition of human faces from surveillance video. In surveillance applications, the system must be robust to changes in illumination, scale, pose and expression. The system must also be able to perform detection and recognition rapidly in real time. Our system detects faces using the Viola-Jones face detector, then extracts local features to build a shape-based feature vector. The feature vector is constructed from ratios of lengths and differences in tangents of angles, so as to be robust to changes in scale and rotations in-plane and out-of-plane. Consideration was given to improving the performance and accuracy of both the detection and recognition steps.

Loss of Synaptic Connectivity, Particularly in Second Order Neurons Is a Key Feature of Diabetic Retinal Neuropathy in the Ins2Akita Mouse

Retinal neurodegeneration is a key component of diabetic retinopathy (DR), although the detailed neuronal damage remains ill-defined. Recent evidence suggests that in addition to amacrine and ganglion cell, diabetes may also impact on other retinal neurons. In this study, we examined retinal degenerative changes in Ins2^Akita diabetic mice. In scotopic electroretinograms (ERG), b-wave and oscillatory potentials were severely impaired in 9-month old Ins2^Akita mice. Despite no obvious pathology in fundoscopic examination, optical coherence tomography (OCT) revealed a progressive thinning of the retina from 3 months onwards. Cone but not rod photoreceptor loss was observed in 3-month-old diabetic mice. Severe impairment of synaptic connectivity at the outer plexiform layer (OPL) was detected in 9-month old Ins2Akita mice. Specifically, photoreceptor presynaptic ribbons were reduced by 25% and postsynaptic boutons by 70%, although the density of horizontal, rod- and cone-bipolar cells remained similar to non-diabetic controls. Significant reductions in GABAergic and glycinergic amacrine cells and Brn3a+ retinal ganglion cells were also observed in 9-month old Ins2^Akita mice. In conclusion, the Ins2^Akita mouse develops cone photoreceptor degeneration and the impairment of synaptic connectivity at the OPL, predominately resulting from the loss of postsynaptic terminal boutons. Our findings suggest that the Ins2^Akita mouse is a good model to study diabetic retinal neuropathy.

Weaving behavior into feature models for embedded system families

Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.

Origin of double dinitrogen release feature during fast switching between lean and rich cycles for NOx storage reduction catalysts

The performance of NOx storage and reduction over 1.5 wt% Pt/20 wt% KNO3/K2Ti8O17 and 1.5 wt% Pt/K2Ti8O17 catalysts has been investigated using combined fast transient kinetic switching and isotopically labelled (NO)-N-15 at 350 degrees C. The evolution of product N-2 has revealed two significant peaks during 60 s lean/1.3 s rich switches. It also found that the presence of CO2 in the feed affects the release of N-2 in the second peak. Regardless of the presence/absence of water in the feed, only one peak of N-2 was observed in the absence of CO2. Gas-phase NH3 was not observed in any of the experiments. However, in the presence of CO2 the results obtained from in situ DRIFTS-MS analysis showed that isocyanate species are formed and stored during the rich cycles, probably from the reaction between NOx and CO, in which CO was formed via the reverse water-gas shift reaction. 

Green Political Economy: beyond orthodox undifferentiated economic growth as a permanent feature of the economy

This chapter outlines the main features of green political economy and the principal ways in which it differs from dominant mainstream or orthodox neoclassical economics. Neoclassical economics is critiqued on the grounds of denying its normative and ideological commitments in its false presentation of itself as ‘objective’ and ‘value neutral’. It is also critiqued for its ecologically irrational commitment to the imperative of orthodox economic growth as a permanent feature of the economy, compromising its ability to offer realistic or normatively compelling guides to how we might make the transition to a sustainable economy. Green political economy is presented as an alternative or heterodox form of economic thinking but one which explicitly expresses its normative/ideological value bases (hence it represents a return to ‘political economy’, the origins of modern economics). Green political economy also challenges the commitment to undifferentiated economic growth as a permanent objective of the human economy. In its place, green political economy promotes ‘economic security’ as a better objective for a sustainable, post-growth economy. The latter includes the transition to a low-carbon energy economy, and is also one which maximises quality of life (as oppose to formal employment, income and wealth), and actively seeks to lower socio-economic inequality. Green political economy views orthodox economic growth as having passed the threshold in most ‘advanced’ capitalist societies beyond which it has undermined quality of life and at best manages rather than reduces socially and ecologically damaging inequalities.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.